Optimal Scheduling of Reservoir Flood Control under Non-Stationary Conditions

Chongxun Mo, Changhao Jiang, Xingbi Lei (), Weiyan Cen, Zhiwei Yan, Gang Tang, Lingguang Li, Guikai Sun and Zhenxiang Xing
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Chongxun Mo: College of Architecture and Civil Engineering, Guangxi University, Nanning 530004, China
Changhao Jiang: College of Architecture and Civil Engineering, Guangxi University, Nanning 530004, China
Xingbi Lei: College of Architecture and Civil Engineering, Guangxi University, Nanning 530004, China
Weiyan Cen: College of Architecture and Civil Engineering, Guangxi University, Nanning 530004, China
Zhiwei Yan: College of Architecture and Civil Engineering, Guangxi University, Nanning 530004, China
Gang Tang: Guangxi Water & Power Design Institute Co., Ltd., Nanning 530023, China
Lingguang Li: Guangxi Water & Power Design Institute Co., Ltd., Nanning 530023, China
Guikai Sun: College of Architecture and Civil Engineering, Guangxi University, Nanning 530004, China
Zhenxiang Xing: School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin 150000, China

Sustainability, 2023, vol. 15, issue 15, 1-22

Abstract: To improve reservoir flood control and scheduling schemes under changing environmental conditions, we established an adaptive reservoir regulation method integrating hydrological non-stationarity diagnosis, hydrological frequency analysis, design flood calculations, and reservoir flood control optimization scheduling and applied it to the Chengbi River Reservoir. The results showed that the peak annual flood sequence and the variation point of the annual maximum 3-day flood sequence of the Chengbi River Reservoir was in 1979, and the variation point of the annual maximum 1-day flood sequence was in 1980. A mixed distribution model was developed via a simulated annealing algorithm, hydrological frequency analysis was carried out, and a non-stationary design flood considering the variation point was obtained according to the analysis results; the increases in the flood peak compared to the original design were 4.00% and 8.66%, respectively. A maximum peak reduction model for optimal reservoir scheduling using the minimum sum of squares of the downgradient flow as the objective function was established and solved via a particle swarm optimization algorithm. The proposed adaptive scheduling scheme reduced discharge flow to 2661 m 3 /s under 1000-year flood conditions, and the peak reduction rate reached 60.6%. Furthermore, the discharge flow was reduced to 2661 m 3 /s under 10,000-year flood conditions, and the peak reduction rate reached 65.9%.

Keywords: non-stationarity; optimal flood control scheduling; simulated annealing algorithm; particle swarm optimization algorithm; Chengbi River Reservoir (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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